Simultaneous allocation of electric vehicles’ parking lots and distributed renewable resources in smart power distribution networks

Amini, M. Hadi; Moghaddam, Mohsen Parsa; Karabaşoğlu, Orkun

doi:10.1016/j.scs.2016.10.006

Cited by 309 publications

(113 citation statements)

References 35 publications

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“…Main findings of this study in comparison with previous work. Harmonics and nonlinearities are ignored (e.g., [14,17,27]). …”

Section: Case 3-installation Of Passive Power Filters (Ppfs) To Impromentioning

confidence: 99%

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Online Coordination of Plug-In Electric Vehicles Considering Grid Congestion and Smart Grid Power Quality

Deilami

2018

Energies

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This paper first introduces the impacts of battery charger and nonlinear load harmonics on smart grids considering random plug-in of electric vehicles (PEVs) without any coordination. Then, a new centralized nonlinear online maximum sensitivity selection-based charging algorithm (NOL-MSSCA) is proposed for coordinating PEVs that minimizes the costs associated with generation and losses considering network and bus total harmonic distortion (THD). The aim is to first attend the high priority customers and charge their vehicles as quickly as possible while postponing the service to medium and low priority consumers to the off-peak hours, considering network, battery and power quality constraints and harmonics. The vehicles were randomly plugged at different locations during a period of 24 h. The proposed PEV coordination is based on the maximum sensitivity selection (MSS), which is the sensitivity of losses (including fundamental and harmonic losses) with respect to the PEV location (PEV bus). The proposed algorithm uses the decoupled harmonic power flow (DHPF) to model the nonlinear loads (including the PEV chargers) as current harmonic sources and computes the harmonic power losses, harmonic voltages and THD of the smart grid. The MSS vectors are easily determined using the entries of the Jacobian matrix of the DHPF program, which includes the spectrums of all injected harmonics by nonlinear electric vehicle (EV) chargers and nonlinear industrial loads. The sensitivity of the objective function (fundamental and harmonic power losses) to the PEVs were then used to schedule PEVs accordingly. The algorithm successfully controls the network THDv level within the standard limit of 5% for low and moderate PEV penetrations by delaying PEV charging activities. For high PEV penetrations, the installation of passive power filters (PPFs) is suggested to reduce the THDv and manage to fully charge the PEVs. Detailed simulations considering random and coordinated charging were performed on the modified IEEE 23 kV distribution system with 22 low voltage residential networks populated with PEVs that have nonlinear battery chargers. Simulation results are provided without/with filters for different penetration levels of PEVs.

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“…Main findings of this study in comparison with previous work. Harmonics and nonlinearities are ignored (e.g., [14,17,27]). …”

Section: Case 3-installation Of Passive Power Filters (Ppfs) To Impromentioning

confidence: 99%

“…The filter components are: RF = 1 Ω, LF = 100 mH, C(5)F = 2.07 µF, C(7)F = 0.84 µF, C(11)F = 0.6 µF, and C(13)F = 0.35 µF [26]. Harmonics and nonlinearities are ignored (e.g., [14,17,27]). …”

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confidence: 99%

Online Coordination of Plug-In Electric Vehicles Considering Grid Congestion and Smart Grid Power Quality

Deilami

2018

Energies

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“…Several studies proposed placing chargers at parking lots and locating stations by taking the limitations of power systems such as power losses, network reliability, and voltage deviations, into account [22][23][24][25][26]. The study in [22] proposed a resource allocation method of EV parking lots, with the consideration of the network reliability.…”

Section: Related Workmentioning

confidence: 99%

A Hierarchical Optimization Model for a Network of Electric Vehicle Charging Stations

et al. 2017

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Abstract:Charging station location decisions are a critical element in mainstream adoption of electric vehicles (EVs). The consumer confidence in EVs can be boosted with the deployment of carefully-planned charging infrastructure that can fuel a fair number of trips. The charging station (CS) location problem is complex and differs considerably from the classical facility location literature, as the decision parameters are additionally linked to a relatively longer charging period, battery parameters, and available grid resources. In this study, we propose a three-layered system model of fast charging stations (FCSs). In the first layer, we solve the flow capturing location problem to identify the locations of the charging stations. In the second layer, we use a queuing model and introduce a resource allocation framework to optimally provision the limited grid resources. In the third layer, we consider the battery charging dynamics and develop a station policy to maximize the profit by setting maximum charging levels. The model is evaluated on the Arizona state highway system and North Dakota state network with a gravity data model, and on the City of Raleigh, North Carolina, using real traffic data. The results show that the proposed hierarchical model improves the system performance, as well as the quality of service (QoS), provided to the customers. The proposed model can efficiently assist city planners for CS location selection and system design.

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“…Specific technologies related to flexibilities include privately owned grid connected batteries such as electric vehicles [2] or larger grid operator owned storage used to improve overall economic exploitation of the feeder. Demand side management optimization in smart grids and efficient smart grid technologies have been thouroughly explored for a variety of use cases [3] [4] [5] [6] [7].…”

Section: Introductionmentioning

confidence: 99%

Optimal sizing and placement of distribution grid connected battery systems through an SOCP optimal power flow algorithm

2018

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. Optimal sizing and placement of distribution grid connected battery systems through an SOCP optimal power flow algorithm. Applied Energy, Elsevier, 2018Elsevier, , 219, pp.385-393. <10.1016Elsevier, /j.apenergy.2017 .008>. Optimal sizing and placement of distribution grid connected battery systems through an SOCP optimal power flow algorithm Etta Grover-Silva 1,2 , Robin Girard 1 , George Kariniotakis 1 AbstractThe high variability and uncertainty introduced into modern electrical distribution systems due to decentralized renewable energy generators requires new solutions for grid management and power quality assurance. One of these possible solutions includes grid integrated energy storage. The appropriate size and placement of decentralized storage is highly dependent on purpose of the battery system and expected operational strategy. However, battery operational strategies are difficult to simulate simultaneously during a sizing and placement planning calculation. The motivation of this paper is to propose an algorithm that is capable of integrating sizing, placement and operational strategies of batteries into an Optimal Power Flow (OPF) distribution grid planning tool. The choice of the OPF approach permits to account for grid constraints which is more adapted for grid-connected storage devices compared to other approaches in the state of the art that are based only on an email balance analysis. This paper presents an alternating current (AC) multi-temporal OPF algorithm that uses a convex relaxation of the power flow equations to guarantee exact and optimal solutions with high algorithmic performance. The algorithm is unique and innovative due to the fact that it combines the simultaneous optimization of placement and sizing of storage devices taking into account load curves, photovoltaic (PV) production profiles, and distribution grid power quality constraints. The choice to invest in battery capacity is highly sensitive to the price of battery systems. The investment in battery systems solely for reducing losses an operational costs was proven not to be cost effective, however when battery systems are allowed to buy and sell electricity based on variable market prices they become cost effective. The assumptions used for this study shows that current battery system prices are too high to be cost effective even when allowing battery system market participation.Keywords: Optimal power flow, storage, smart grids, renewable energy, distribution grid planning Nomenclature Parameters η st Charging and discharging efficiency of the battery system P pv,j,t Ideal PV production for node j at time step t S pv,j,t PV maximum apparent power flow at node j V j Voltage maximum at node n V j Voltage minimum at node n c inv stInvestment costs of the battery system for the nominal capacity in e/MWh-day Operations and maintenance costs of using the battery system for the power use in e/MWh-day c e,tPrice of electricity at time step t I max Total capital cost limit of project P ld,j,t Active power load at n...

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Simultaneous allocation of electric vehicles’ parking lots and distributed renewable resources in smart power distribution networks

Cited by 309 publications

References 35 publications

Online Coordination of Plug-In Electric Vehicles Considering Grid Congestion and Smart Grid Power Quality

Online Coordination of Plug-In Electric Vehicles Considering Grid Congestion and Smart Grid Power Quality

A Hierarchical Optimization Model for a Network of Electric Vehicle Charging Stations

Optimal sizing and placement of distribution grid connected battery systems through an SOCP optimal power flow algorithm

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